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          <h1 class="post-title" itemprop="name headline">缓存简单介绍

              
            
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              <time title="创建时间：2019-07-19 16:31:06 / 修改时间：16:57:46" itemprop="dateCreated datePublished" datetime="2019-07-19T16:31:06+08:00">2019-07-19</time>
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        <h2 id="缓存概念"><a href="#缓存概念" class="headerlink" title="缓存概念"></a>缓存概念</h2><h3 id="缓存穿透"><a href="#缓存穿透" class="headerlink" title="缓存穿透"></a>缓存穿透</h3><p>缓存穿透是指攻击者不断发起查询缓存和数据库中都没有的数据，导致压力全部落在数据库，导致数据库压力过大。</p>
<h4 id="解决方案："><a href="#解决方案：" class="headerlink" title="解决方案："></a>解决方案：</h4><ul>
<li>接口层增加校验，如用户鉴权校验，id做基础校验，id&lt;=0的直接拦截；</li>
<li>从缓存取不到的数据，在数据库中也没有取到，这时也可以将key-value对写为key-null，缓存有效时间可以设置短点，如30秒（设置太长会导致正常情况也没法使用）。这样可以防止攻击用户反复用同一个id暴力攻击。  </li>
</ul>
<h3 id="缓存击穿"><a href="#缓存击穿" class="headerlink" title="缓存击穿"></a>缓存击穿</h3><p>缓存击穿是指缓存中没有但数据库中有的数据（一般是缓存时间到期），这时由于并发用户特别多，同时读缓存没读到数据，又同时去数据库去取数据，引起数据库压力瞬间增大，造成过大压力。</p>
<h4 id="解决方案"><a href="#解决方案" class="headerlink" title="解决方案"></a>解决方案</h4><ul>
<li>设置热点数据永远不过期。</li>
<li>读数据库时加互斥锁，写入缓存，其他等待线程就可以从缓存读取  </li>
</ul>
<h3 id="缓存雪崩"><a href="#缓存雪崩" class="headerlink" title="缓存雪崩"></a>缓存雪崩</h3><p>缓存雪崩是指缓存中数据大批量到过期时间，而查询数据量巨大，引起数据库压力过大甚至down机。和缓存击穿不同的是，缓存击穿指并发查同一条数据，缓存雪崩是不同数据都过期了，很多数据都查不到从而查数据库。</p>
<h4 id="解决方案：-1"><a href="#解决方案：-1" class="headerlink" title="解决方案："></a>解决方案：</h4><ul>
<li>缓存数据的过期时间设置随机，防止同一时间大量数据过期现象发生。</li>
<li>如果缓存数据库是分布式部署，将热点数据均匀分布在不同搞得缓存数据库中。</li>
<li>设置热点数据永远不过期。</li>
</ul>
<h2 id="内存缓存框架"><a href="#内存缓存框架" class="headerlink" title="内存缓存框架"></a>内存缓存框架</h2><table>
<thead>
<tr>
<th>比较项</th>
<th>ConcurrentHashMap</th>
<th>LRUMap</th>
<th>Ehcache</th>
<th>Guava Cache</th>
<th>Caffeine</th>
</tr>
</thead>
<tbody><tr>
<td>读写性能</td>
<td>很好，分段锁</td>
<td>一般，全局加锁</td>
<td>好</td>
<td>好，需要做淘汰操作</td>
<td>很好</td>
</tr>
<tr>
<td>淘汰算法</td>
<td>无</td>
<td>LRU，一般</td>
<td>支持多种淘汰算法,LRU,LFU,FIFO</td>
<td>LRU，一般</td>
<td>W-TinyLFU, 很好</td>
</tr>
<tr>
<td>功能丰富程度</td>
<td>功能比较简单</td>
<td>功能比较单一</td>
<td>功能很丰富</td>
<td>功能很丰富，支持刷新和虚引用等</td>
<td>功能和Guava Cache类似</td>
</tr>
<tr>
<td>工具大小</td>
<td>jdk自带类，很小</td>
<td>基于LinkedHashMap，较小</td>
<td>很大，最新版本1.4MB</td>
<td>是Guava工具类中的一个小部分，较小</td>
<td>一般，最新版本644KB</td>
</tr>
<tr>
<td>是否持久化</td>
<td>否</td>
<td>否</td>
<td>是</td>
<td>否</td>
<td>否</td>
</tr>
<tr>
<td>是否支持集群</td>
<td>否</td>
<td>否</td>
<td>是</td>
<td>否</td>
<td>否</td>
</tr>
</tbody></table>
<ul>
<li>对于ConcurrentHashMap来说，比较适合缓存比较固定不变的元素，且缓存的数量较小的。虽然从上面表格中比起来有点逊色，但是其由于是jdk自带的类，在各种框架中依然有大量的使用,比如我们可以用来缓存我们反射的Method,Field等等;也可以缓存一些链接，防止其重复建立。在Caffeine中也是使用的ConcurrentHashMap来存储元素。</li>
<li>对于LRUMap来说，如果不想引入第三方包，又想使用淘汰算法淘汰数据，可以使用这个。</li>
<li>对于Ehcache来说，由于其jar包很大，较重量级。对于需要持久化和集群的一些功能的，可以选择Ehcache。笔者没怎么使用过这个缓存，如果要选择的话，可以选择分布式缓存来替代Ehcache。</li>
<li>对于Guava Cache来说，Guava这个jar包在很多Java应用程序中都有大量的引入，所以很多时候其实是直接用就好了，并且其本身是轻量级的而且功能较为丰富，在不了解Caffeine的情况下可以选择Guava Cache。  </li>
</ul>
<p>总结一下:如果不需要淘汰算法则选择ConcurrentHashMap，如果需要淘汰算法和一些丰富的API，这里推荐选择Caffeine。</p>
<h2 id="分布式缓存框架"><a href="#分布式缓存框架" class="headerlink" title="分布式缓存框架"></a>分布式缓存框架</h2><p>这里选取比较出名的分布式缓存来作为比较，Memcached，Redis. 不同的分布式缓存功能特性和实现原理方面有很大的差异，因此他们所适应的场景也有所不同。  </p>
<table>
<thead>
<tr>
<th>比较项</th>
<th>Memcached</th>
<th>Redis</th>
</tr>
</thead>
<tbody><tr>
<td>数据结构</td>
<td>只支持简单的Key-Value结构</td>
<td>String,Hash, List, Set, Sorted Set</td>
</tr>
<tr>
<td>持久化</td>
<td>不支持</td>
<td>支持</td>
</tr>
<tr>
<td>容量大小</td>
<td>数据纯内存，数据存储不宜过多</td>
<td>数据全内存，资源成本考量不宜超过100GB</td>
</tr>
<tr>
<td>读写性能</td>
<td>很高</td>
<td>很高(RT0.5ms左右)</td>
</tr>
</tbody></table>
<ul>
<li>Memcached：其吞吐量较大，但是支持的数据结构较少，并且不支持持久化。</li>
<li>Redis:支持丰富的数据结构，读写性能很高，但是数据全内存，必须要考虑资源成本，支持持久化。  </li>
</ul>
<p>总结:如果服务对延迟比较敏感，Map/Set数据也比较多的话，比较适合Redis。</p>

      
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